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Using proactive metrics for support operations

Using proactive metrics for support operations

Kay - Welcome to experience dialogue. In these interactions. We pick a Hot Topic. That doesn't really have a straightforward answer. We then bring in speakers who bring their skin, but approach it in very, very different ways. This is a space for healthy, disagreements and discussions, but in a very respectful way, just by the nature of how we have conceived this, you will see very passionate wisest of opinions, friends. Having a dialogue. And thereby even interrupting each other or finishing each other's sentences at the end of the dialogue. We want our audience to leave with valuable insights and approaches that you can try at your workplace, workplace and continue the discourse in our social media channels. It's a pleasure for me to introduce Charlotte after,u , having had a few discussions with Charlotte, but we were right off and we were picking up exactly where we picked up from. And The discussions I've had with Charlotte really talked about the underlying Foundation of why we did the experience dialogue. So it's a pleasure to welcome Charlotte here to have a discussion with us. Thank you for coming. I
Charlotte - Thank you for having me.
Kay - Charlotte and I will be working together and we'll be having a discussion about a framework on how to look at support operations, data from the eye of proactive support shall be talking and shall be taking us through which support data matters. Which ones need which metrics need to be retired. And which metrics need to evolve and where we do need to. Look at this. Data will be taking some very practical examples and on how to support operations teams. Be transitioning themselves to proactive metrics with that. Charlotte, I'll hand it over to you.
Charlotte - Thanks so much kay. This is me. I am Charlotte wouldead of support a snow plow. That's a behavioral data platform. That allows you to purposefully create behavioral data AI. I have been in support in deeply technical organizations for a lot of years and doing everything in and around support, for a lot of years. I'm sorry to So, say please years, to say both in technicalHP tech companies,I've been 18 years, fully remote leading, technical sport teams.I look after a little website, little corner of the internet called customer support, and that is also the home of my podcast, which is customer support,and it's all about support and customer experience.
Kay - Hey, so many years of experience here too, I'm Bill, teensscale, businesses, add some Adobe, and also have done startups.I was just counting Charlotte, actually, have done more time with startups.Now,and that time has surpassed the corporate experience. And every time, , in the Adobewe took those Adobe Connect to the cloud. And number, one thing that comes up as soon as a product goes to the cloud is doubled because it becomes a very integral part of the organization. AndI still remember, we would rotate our Architects and we would rotate our senior Engineers to take support calls every month and that was something that we would do just to understand the pulse of the customer. And so I'm actually super excited to have this discussion to really talk about, , how can we get the pulse of the customer in many other ways? Thank you so much.
So with Ascendo what we want to do if we want to be able to provide meaning to every interaction. So Charlotte and I are having an interaction here. How can we do the same thing as a company? At the end of the day, it has many, many, many interactions with customers and the interactions happen from website, forums, Community,chatbots emails, phones, And slack, teams, many options, that B2B companies are now having interactions with customers. How can we get provide meaning to all of those interactions is really what we are focused on and that's why this topic is very, very close to your heart.
Charlotte - Indeed ,I think one of the things that we often do and support is we want our meaningful interactions to mean something to the business as well. Don't we? We, we talked about, we have these big desires, we have big goals. What support leader doesn't want to see at the table and use those meaningful interactions to unlock the key to customer success. And Through that, , we often So we're off to contribute to the business through driving efficiency and contributing to product and contributing to revenue. These are all really big goals.And so how do we get that seat at the table is something that is often asked for support to do that. We often focus on outward metrics that the business understands.So we talk often about customer satisfaction, our average handle time in terms of efficiency, mean time to resolution and how it contributes to customer success and customer satisfaction. But these are all really lagging indicators.These are all metrics that lag behind our ability to provide more meaningful reactions for our customers, I think.
Kay - Yeah. Those are the metrics that most support leaders are looking at today, right? Charlotte.
Charlotte - Absolutely. They are. They are. Take my van out of your day and think about not reacting to those external metrics. Those external metrics are very important in terms of being able to give a narrative around the health of what we're doing, but actually how we can use turndata internally within our teams within our business trunk functions to be ahead of the game, stop reacting to those lagging indicators,and actually proactively create data internally. That helps us. Those indicators. AndI think it's really important just to take a step back and understand what the difference is between metrics and data because we use them interchangeably quite a lot and metrics is the word that strikes fear into every support person's heart, right? But let's just be really clear. What we mean metrics are the parameters that we might use with quantitative be all measures in and of themselves. So your average handle time is a metric but it's made up of data points. So what data really is, it's actually the underlying numbers in information that we produce and collect and metrics what we produce from that data. So when we think about data, there's a lot out there, we might have time data,we might have, in fact, data from our health centers. We've got product analytics and we'll dive into some of those shortly, I'm sure. ButLet's just think about data that can be created purposefully. And with a structure that we understand, and I would call that data Creation with snowplow, that data creation or as a byproduct of all of our other systems. So This this term that we're beginning to use of data exhaust data, that is a byproduct that just happens to be there because of interacting with systems all the time though. that's our data at the low level numbers.
Kay - One of the wonderful things that during that first discussion,Charlotte isometrics, why doesn't it work anymore? The reason it doesn't work anymore is data has become huge, not all the data that challenges talked about, right? The structured data, the byproduct data, exhaust data, the unstructured data, they all have become large and focusing just on metrics means focusing only on customers who have filled in some of those cervix and that me be only a percentage or a sliver of a customer population that's number one. Number two is, we are not getting the level of color that when we don't include all of the interactions, we are only getting a biased view from it. There is squeaky customer or from a high paying customer or something like that. Instead of everybody and getting the feedback or getting the insights, from all of the customers becomes very important, not just for SAAS, but also, Also, for non SAAS for even traditional come. that something? What we have seen. So what this data and whatCharlotte is alluding to, with respect to the difference between the metrics and data is seems to be very or knowing the difference between metrics and data, seems to be the underlying Foundation of always supporting moves from proactive to from reactive to proactive.
Charlotte - Absolutely and how understanding the difference and how you react to and operationalize around metrics versus data, allows you to do the things that you very kindly outlined on this slide. And That is like begin to look for patterns, begin tomine, our customer data, make it better, use it to interact with our customers in a more meaningful way. As you said at the touch but also the, , internally again driving Operational excellence. If we concentrate on the operational excellence of our business functions. Then it hasan onward effect in terms of driving value. For our customers in improving the customer experience and therefore, in improving. All of those lagging indicators that we outlined at the start, our customers' satisfaction are handled times and, and everything else.
Kay - Yeah. That's it was wonderful to see this report from Gardner on top priorities for customer service and support leaders actually just the 2023 report, and the number you can see that it's mining customer data is important primarily from helping out representatives from providing that intelligence that's needed for taking in that seat that Charlotte was earlier talking about at the table, or the support leaders to be with the rest of the leaders, to be the word true, what the customer, and to get Rest of the organization to be more customer centric.uh So I think it's really important.Therefore we've already defined metrics and data, data can feel like an almost, will it really is an inexhaustible landscape of numbers and information. And it,
Charlotte - I think it's quite often difficult to know where to start. Particularly, when we talking about actually driving actions from it.So one thing that I like to do is think about data in three different ways.There's some snow plow. language in here and there's some Meyer language in here, but I Think it's really important to think about The quality and the usefulness of the data that we have, and what we can do for it, and what we can drive from it. So first of all data, that is best for light work, the less reliable or anecdotal, this is actually not that actionable,uh it's valuable. If you are able to take it and appreciate what you can do with it,and I would say the anecdotal data,or data that's on reliable data. That's about feelings and everything else. Is really an inspirational thing. So those the things that might trigger a research project or might drive you to go and collect more accurate data and more structured data and so on the stuff that can really Drive action exhaust, data might actually be accurate, you might have a whole landscape of numbers at your fingertips. But If they are, nearly the byproducts without any thought given to exactly what then the meaning of those numbers, It is of everything you're doing. They can be unfocused and disparate, and really, that's what we need to think, not about research projects but about brain structure andAnalysis. And finally, the most positive end of this is the data Jizz that is really created specifically for a purpose.This is where I love to play around because I love creating data knowing what the question is that I want to answer. South thinking about data is, what the how its structured allows you to create action. If you have the question in mind, what data you need to collect, , how to structure it. And you can use that to answer your questions and therefore driver actions.
Kay - That's so beautifully said because one of the things that then these targets and do the reform of we were talking to initially, its customers. The first thing was hey what we have these these questions that we could just ask those questions and we get those answers, right? And provide the patterns for the Soma, it's that questioning that Curiosity, that's coming in,not to just look at the metric but to say okay, what does the data say? How do I need to carve out the story? What, , that Curiosity stemstarts in this entire exploration? So I love how you said it,Charlotte and I think thank you.
Charlotte - And I think what's really nice about sending these three layers is that you can approach this from either end, , you can, you can you have a kind of idea, but you have to begin and go and see what you, what date you have. So you might start at the anecdotal and like, this is given Confront him, , I'll go find some things that sort of support it and then I'll dig the Diabolical data we've got and then I need to bring some some structure to really answer the question or you've got a really specific question and you can answer it because you've invested in that structure already, which is great, but you might enrich it with a bit of exhaust age. But very particularly with the anecdotal and get more narrative from the, from the fuzzy are end of the data spectrum of your life. I really like that part. and so, I think in,Helping other leaders out there. Thinking about this is really important to me because it's not clear often I think at the start, when you think about your data Journey,exactly how you approach all of those things. And one thing I've asked other or other leaders from other organizations in the past to do is just take this two step approach literally list everything out that you have Even dated something that you don't think is data, it is Data. So, including all of those, your slack conversations, people's feelings comments that you see in survey responses, this is all data,but list it all out and then just take the take the time to categorize it so that you give it the appropriate way so that, , where you can start to ask questions andwhere you need to bring data on in this, , down the ladder if you like to. To actual action at the bottom. So you might have this anecdotal but we've all got it. We've all got buckets of slack conversations about our opinions and everything else there, but they do Inspire the research, don't they?
Kay - Yeah, that's what. Yeah. Gear Generation. Yes. Yeah, one of the customers actually had an outsource there at one Gmail's Royal one team.They still follow theirL1 model 0, of the swarming model. But it's fascinating that they were talking about people. Who has posted notes in their computers and all of that is data, right? So, all of that is knowledge that is sitting in somebody's and unstructured and that needs to be coming in and there's a wealth of information from the front end of people who are talking to customers that can be piped in all the way up to the escalation.
Charlotte - Yeah, yeah, absolutely, absolutelyPostit notes.uh I love a screen surrounded by post loads. It tells me. to pay attention to it, take it to a whole nother level. Then like, we again in help centers, we're producing data all the time, but we don't necessarily pay much attention to most of it but we are generating time stands. We are replying to tickets, we are resolving tickets. Actually, most questions have an answer and that's a fairly good answer. It's not. It's not structured well enough necessarily to mind immediately. Idiots lie, but usually answers are having an attachment to the question, ? So, ticket resolutions are what I sort of consider to be exhaust data as well. What you need to do with exhaust data as I said before is really just spend the time bone to give it further shape to , analyze it and decide. What's useful? What's not what you can, what you need to do is little to as possible to make actionable it and What needs further work and this is where the analysis comes in is on all that. Seoul station. Now, if you're very lucky and you work for a company like smoke now, or we spend some time with me, you'll know how passionate I am about creating data that asks, where the answers questions from the get go. And so, the last one here is where I spent most of my time and that is creating data and pulling data points together to really specifically answer questions and therefore Drive actions. And the structure comes in all sorts of ways. , it's around. It can be around understanding how the different data points that you've got fit together and what, what narratives and actions you can drive from bringing two pieces of data together that never existed together before or it's, or it's also possibly bringing structure to something that didn't have stretch before. And for me I'm deeply passionate about having a pretty straight ticket tagging taxonomy.So that's one that we have a very structured approach to a snowplow as well. So it's very many of these data points, what we can drive actions from
Kay - Absolutely. And what you're really talking about is getting the data ready as time series data, right? So then it's time data. What happens when at what given point across the interactions that can be mined for AI, Rich, right. So,I'm just going to tag onto what Charlotte was mentioning and call out the various types of data. So we have the transactional systems that the CRM,the bug, tracking the knowledge pieces and all of that. On top of it, we have everyday interaction that comes in from the various channels. And on top of it we have the data exhaust that comes in also from all the logs and the product usage in all of it. What is interesting? Here is Tithing in those pieces of information, trying to find those patterns to answer those curious questions. So what kind of problems are really happening? If you can,what parts of the product right now? Or a month ago, where is it? Increasing,which part of it is increasing, who isMooney? Who within the team is an expert in these kinds of problems?What? And which of these Solutions are being most effective? Or a customer. And how can we take that piece of knowledge that is in a human's mind and used to resolve or train somebody else who's coming in on board, right? So it's really, if systems would be, it doesn't we call it human,human machine interaction, right? So, essentially what we mimic is how humans solve problems. So it's very exact Charlotte said which is, yes, there is a solution for every kind of problem. And even if there is no solution, how do we humans? Think about it, right? Oh, this is very similar to something. I did three months ago, and of course a little bit of the solution, let me dig a little bit more, right? So, it's that mimicking of the data that it provides to the agents, to be able to solve things faster.
Charlotte - Absolutely, absolutely. And that's what we want to do. All three solve things faster,better, more efficiently and with more value to the moment. Yeah. So I thought it would be useful to describe briefly what this looks like in reality. I will say that the charts on the right are anonymized and fictional but there are taste of the kind snowplow. of things that look at And so how we, how we think about this and how we've operationalized around the state today,which is subtitled talk came from,it is really about what we, what data we have, how we structured it, how we bring it together and what we added, actually,to answer the questions, the big part of going through that process of understanding. What data you have is understanding what data is missing. Then you need to answer those questions. Sowhen I joined smoke, now, one of the first things that we did was begin tracking time and support, which I know is controversial. I know it's controversial, but it's important to me and to us because it's a very, very complex ecosystem to support and it's a really, really valuable and insightful data point for a number of reasons. And so, in tracking our time, I was filling one of those missing gaps. It's one of those missing actionable data onesand beginning to drive data. Better created and joined up data. So I love this phrase,which I buried in the text here. But, , you can validate hero hypotheses or calibrate emotional readout which is take take, all those feelings about all of the pain. We're feeling and supporting our pain. Our customers feeling and actually make them validate them .Is this hypothesis that there's this thing? This PostIt note is this bit of feedback valid in the great landscape of things.uh It of course it's valid actually because it's somebody's opinion or feeling. But is it actionable can actually do anything with, , and in and in calibrating and being able to compare one thing against the other, you can drive actions. So that's what gets teams out of the firefighting mode. It really does because it's very empowering actually. You're absolutely right knowing that you have information on how you can draw on any coin and we repeatedly come back to this. And even if we have a question from six months or two years ago,the data stays there, we don't throw away. We can because we're a data company, we love dashboards, everything is live, everything is continually updated. So we can go back and ask the same question and see how the landscape has changed and to You That we have more of a dashboard and this is a little taste of it as a service, all kind of fictional.But it just has things. Very visually, it's important for us to be able to and hotspots spot patterns and allow us to dive in very quickly. So, to that in turn going back to do the process that I mentioned, we create all of our data very intentionally drums sources together, outside of the CRM.So we do have Salesforce then just data, we've got our product and their tits.We've got ourtime tracking Key. And a number of other pieces that we can pull together. Other. So some examples I've given here or, ,how much, how many not just, how many tickets are we getting an objective taxonomy, and what's the Applefrom my team and beginning to resolve, a certain type of problem? And by effort, I mean, ours, I don't mean elapsed hours for a ticket. I don't mean resolution time. I mean actual effort because we all know resolution time is elastic. Weit depends how responsive your customer is, , you might have to go off to another third party. But effort is a really good indicator of the complexity of a problem , I think. And so I could become more and more important as groups of people are working together to fix problems like this warming model.Right. Exactly. Exactly. So we can respond to that. We can respond to ing if this is more complex than we think it is weird. Again, it's calibrating emotional reader, something feels a bit painful but actually is it five seconds out of all day and it's just not worth, like engineering around or We do something different actually quite a lot of the way I approached a chore, it's should we be doing something different? to that end we look at things like which of our customers I dug out some fictional customer names,they're from The Simpsons and everywhere the organization's there. But this perspective of like, again, the effort involved in supporting customers is really insightful. Cuz it tells us if a customer is, it's about being proactive. It's about again, getting him ahead of the game. We can see when customers are starting to need more of Time, need more of our support, we can get ahead of the game by these visual clues. That said, I should probably spend some more time investinglike, more quality interactions with a particular customer. For example, maybe I get on a call with and maybe we just do a few more coaching or Kohl's or something like that. Customer, I get them. And that, and the, the chart underneath the kind hand in hand in that respect because, , a big overhead is tickets wandering around your team, looking for the answer. So I love to drive independent ticket resolutions. So how many of my tickets are sold by one? That's awesome. And can deeper dive and say which of my people on my team are seeking more help, ,which people are providing more help. So it's beginning to identify Stars mentors and people who do need just maybe a little bit more knowledge and support which in the early days we know that it's supertight onboarding is really critical particularly in ain a very technical environment and then of course in terms of operational excellence understanding stretched, my tears or isn't on any month a year. How, how am I matching my resourcing against my incoming load is really important manage, operational excellence. That's a sample of some of what we've actually doing day to day.

Kay - I was actually just hearing you did actually help you say as Already better, right? So whether it is the operational aspect of it, whether it is going to an agent and saying here is the reason why, ,I would love you to take this training. It becomes very collaborative with the rest of the organization to March forward on this customer mindset, right?
Charlotte - It really is, and more than anything, it helps you tell those stories before. the customer tells you, though, before the customer says to you, I didn't have a great experience. Yeah. And that's already more than getting ahead of the game driving. Those proactive interactions are proactive actions from this data because you can respond to it very quickly, and this misleading information, not lagging emotions. And so I think, those actions, I just wanted to throw out a few ideas and I know you'll have some thoughts on this as well because we taught quite a lot about this slide when I was pulling them for your support driventalk. And after two so I eat. This is awesome. Yeah, please. I went full everything on here but I think what's really interesting is just just how Butuh how how many different parts of your operations,you can touch with this approach and you can improve with this approach,and that you can, , you can modify and positively, and proactively modify opt is where we're getting to ultimately with all of this work.
In terms , the technical side reduces humans in the loop. So reducing the tasks that could be Automated away and given the team better quality of their Professional Day,improving internal, tooling, and reducing friction. These are all really positive experiences. You support me organizationally managing your customers better efficiencies creating and getting the right people the right problems. , I think these are all really good ways of really good things to think about in terms of how you interact and contribute to the rest of the compliment or rest of your own organization. And then from the point of your business growth you'll help your customers by reacting. And adjusting the way you solve problems and as you said before, adjusting what knowledge, you use it, that definitely contributes really well and your ability to get bored quickly.
Kay - Yeah. I actually would love to shallot. start seeing if I'm a Charlotteto all the support leaders. Start being curious,So it's the questions that you are asking here. That's making you look at the data and coming up with answers. And the interesting part is there is a question that someone has asked saying how much data is actually needed and will start ups, and or companies with new products will they have enough data to do this kind of analysis, and from an AI aspect? Absolutely. Yes. Because until you start thatCuriosity and questioning you don't even know, there are always going tobe.
This is a journey, there is always going tobe data gaps, son. You will encounter those data gaps only when you start on the journey. So when you start in the journey is when you would recognize, oh, here are the gaps and I can fill those in. In models are also when it is done, these are proprietary models you have done for support. So they are very good at looking at even smaller pieces of data and coming up with answers for a lot of these questions. So Charlotte, do you have any comment on the amount of data before we go into the slime?
Charlotte - I think it's a super, super interesting question and I think there is no single answer to that. I think it's exactly as you said and as was describing before, you just have to get started because until you understand what data you have and how you move it along that chain. So well structured questions, that question answering data,, that drives actions. You just don't know. And I think, , in terms of the number of data points, you don't need much. I think you'll like it, but I think you have to get started and I think the important thing is beginning to data along that move your German.
Kay - Yeah. Charlotte you talked about, which is,um here are the types of questions and just extends into the type of questions across the various support teams, what the leaders are looking for, were the agents are Looking for and customers or even the supply chain and the logistics teams are looking for course, hardware, and software companies. So across the board, there are all these curiosity and all of these questions and we had the discussion with and last last time, and she was alluding to an example and she was talking about an example where even with a small amount of data, she was able to get answers for a lot of these questions. It's looking at the similar data from various angles and looking at the patterns across those, to be able to come up with good arguments. That can help say a story, right? So that's all I wanted to cover here.
Charlotte - Yeah. It's about looking at small amount of data from different angles is all about structure.It's like what is the thing that I need to extract? And how do I fit this together and you don't need a lot of data, two or three. Disparate points is Entity to give you lots of different pictures. So, , the final thing and I know we both, we probably both want to talk this Like A and B, but but B me be the key takeaway me is the leavers column, ,it's what actions can. Try it. And I talked about some of it. When I looked at my dashboards, we talked a little bit about it on the following two slides where we just drew out. Some of the kinds of things that we'll be looking at. These are really the questions aren't they? How do I, how do I do this thing? How do I improve this thing? How do I? And, ,and I think for me the Believers, the actions that you can prolonged a bigare all in that column and they're allHave to be, they all have to have a pounding and strong data and in a strong approach to data and well structured data because otherwise if you pull a Lie by you without knowing exactly what you're pulling, you're not going to get measurable outcome for it from it and you're not going to see.mean, every one of your impacts on those on the right hand corner as a number by to it. You can't apply a number without founding in data. And for me, that's what I take away from this, that the passengers Civil actions are great, but you need data to be able to measure the album.
Kay - Yeah, so there are a lot of questions here. I'm trying to peel some of these questions too. So there is a couple that is appropriate to what you're talking about here. So can you comment on what data may become important, given the projected, , Global recession. That's our way.
Charlotte - I mean I think unfortunately we're all having a little bit. Operating more efficiently efficiently and there's big term of operational excellence huge part of that is operating efficiently and therefore to I mean it's the age old story don't think that the while we are in the throes of a recession I don't think this story really has changed a great deal of support ever since I've been doing sport which is how do you more with less and that's that's what every support leader will. It will be dancing but just more so now than ever before. So I think that in terms of Creating Efficiencies In your business function.Unfortunately, sometimes that means people, but, but actually, it doesn't necessarily mean lay-offs. It means how can you provide a good or better service with what you have? How much time do you invest in improving things operationally, , taking team pain away so that they're able to provide a more valuable experience of all of these things. And I think it just comes down to doing more with less, and more. Can mean many things.
Kay - It's not just about load exactly. And it's also not just what, , makes existing people work hard, and it's also making them work smart by providing them and empowering them with tools and techniques that makes their job easier so they can do more with less in a smart way, right? So Absolutely absolutely. The other question is,there are so many ways at the end of the day, , even this chart Talks about increasing customer support experience, right? So because they enter your company's marketing towards increasing some customer experience,but if there is one leading indicator in here, that you would like to pick for the support team. I think that's what this question is about. Does it just see it? I need to pick one forward looking indicator. What would that be?
Charlotte - I would look at the value that your team can add to every interaction. That's going to vary so much organization to organization. But I think that surfacing Information data,uh actionable data,from across your business, to your support team is really critical in maximizing the bag. You are your customers. Our been for every interaction with that support team.And so I think you have to figure out what value add looks like to your organization.And I'm sorry, this is a little bit of a wooly answer but, but that's just so different, .It can be, , how do, how do we process returns faster? Or how can I help a customer to a next to use case or anything, in between, where I,I think figuring out what your team can do to add value to customers into action. Ins. And what that looks like to your organization.
Kay - I would agree with. The reason it's different from what you're saying is because organizations are in different stages in this journey. So that's why it is different, right? So for some, we are starting off with,,bringing inuh collecting pieces of knowledge. For some it is I'm starting to do some service, for some it is I want to empower my agents first. Or something that eats it. So it's different for different companies. So absolutely. Yes. The I know we have a few more slides to go through so we should do that General pet because I didn't get the next question. I guess the next one just asked everybody else out there. 2023. Sofor Or in five of them are looking at customer value and enhancement. And that's the one that Charlotte was talking about: what is the customer value? I can provide support as a teen and how can I tell a story about that to the rest of the organization? And how can I make the rest of the organization March along with me? That's really the Crux of what a support leader should be doing, and with that I think that pretty much speaks to the slide.
Charlotte - Absolutely. And I just got one thing to that, which is that, as we said before, understanding what customer value looks like to an you tell those stories back in the business, relies on you being able to measure what customer value is as well and believe, as we can pull it. So it's super that I learned portables.
Kay - What I learn from this conversation, Charlotte, starts with the Curiosity of a question, right? So, and then align the data, and what data can answer those questions then that data in itself will come up with a story on what needs to be done to improve support operations. And then how do you move forward with that story to bring the rest of the Ization and the team on board, right? So that's the path that you clearly laid out in this conversation.Thank you. Thank you very much for providing that insight. And thank you very much for having that framework for all support leaders. Like I said, I would love for everyone to be your shelter. So I'm starting to ask questions.
Charlotte - That's great. Thank you so much for having me case and pleasure, and very happy to continue the conversation with you or anyone else who happens to be listening.
Kay - Thanks Charlotte.

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